Importing neccesary libraries and functions¶
In [98]:
cd /content/drive/Shared drives/USV RECORDINGS/
In [99]:
from __future__ import print_function
from keras import backend as K
import keras
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.metrics import classification_report, confusion_matrix
import random
from tensorflow.keras import layers, models
from keras.layers import UpSampling2D,Input,LeakyReLU,MaxPooling2D,Dropout,Lambda,add,Activation,Concatenate,Dense,Flatten,Conv2D
from keras.utils import plot_model
from keras.models import Model
# from keras.layers.convolutional import Conv2D
from keras.callbacks import ModelCheckpoint,EarlyStopping,ReduceLROnPlateau
import seaborn as sns
def set_callbacks( name = 'best_model_weights',patience=8,tb_base_logdir='./logs/'):
from keras.callbacks import ModelCheckpoint,EarlyStopping,ReduceLROnPlateau
cp = ModelCheckpoint(name +'.h5',save_best_only=True)
es = EarlyStopping(patience=patience,monitor='val_loss')
rlop = ReduceLROnPlateau(patience=5)
return [rlop, es, cp]
def cm_analysis(y_true, y_pred, filename, labels, ymap=None, figsize=(5,5)):
"""
Generate matrix plot of confusion matrix with pretty annotations.
The plot image is saved to disk.
args:
y_true: true label of the data, with shape (nsamples,)
y_pred: prediction of the data, with shape (nsamples,)
filename: filename of figure file to save
labels: string array, name the order of class labels in the confusion matrix.
use `clf.classes_` if using scikit-learn models.
with shape (nclass,).
ymap: dict: any -> string, length == nclass.
if not None, map the labels & ys to more understandable strings.
Caution: original y_true, y_pred and labels must align.
figsize: the size of the figure plotted.
"""
if ymap is not None:
y_pred = [ymap[yi] for yi in y_pred]
y_true = [ymap[yi] for yi in y_true]
labels = [ymap[yi] for yi in labels]
cm = confusion_matrix(y_true, y_pred, labels=labels)
cm_sum = np.sum(cm, axis=1, keepdims=True)
cm_perc = cm / cm_sum.astype(float) * 100
annot = np.empty_like(cm).astype(str)
nrows, ncols = cm.shape
for i in range(nrows):
for j in range(ncols):
c = cm[i, j]
p = cm_perc[i, j]
if i == j:
s = cm_sum[i]
annot[i, j] = '%.1f%%\n%d/%d' % (p, c, s)
elif c == 0:
annot[i, j] = ''
else:
annot[i, j] = '%.1f%%\n%d' % (p, c)
cm = pd.DataFrame(cm, index=labels, columns=labels)
cm.index.name = 'Actual'
cm.columns.name = 'Predicted'
fig, ax = plt.subplots(figsize=figsize)
sns.heatmap(cm, annot=annot, fmt='', ax=ax)
plt.savefig(filename)
def Remove_None_Data_and_lowering_Labels(data,labels):
data = [j for i,j in enumerate(data) if j is not None]
labels= [j for i,j in enumerate(labels) if j is not None]
labels = [str(i) for i in labels]
labels = [i.lower() for i in labels]
return data, labels
def build_model(num_classes = 2):
inp = Input(shape=(img_rows, img_cols,1))
x = Conv2D(32, (3, 3), padding='same')(inp)
x = LeakyReLU(alpha=0.2)(x)
x = Conv2D(64, (3, 3), padding='same')(x)
x = LeakyReLU(alpha=0.2)(x)
x = MaxPooling2D(pool_size=(2, 2), padding='same')(x)
x = Conv2D(64, (3, 3), padding='same')(x)
x = LeakyReLU(alpha=0.2)(x)
x = MaxPooling2D(pool_size=(2, 2), padding='same')(x)
x = Conv2D(64, (3, 3), padding='same')(x)
x = LeakyReLU(alpha=0.2)(x)
x = MaxPooling2D(pool_size=(2, 2), padding='same')(x)
x = Dropout(0.25)(x)
x = Flatten()(x)
x = Dense(128)(x)
x = LeakyReLU(alpha=0.2)(x)
x = Dropout(0.5)(x)
x = Dense(num_classes,activation='softmax')(x)
return Model(inputs=inp, outputs=x)
def Take_Syllables(Syllable_name,Data,Labels):
ind = Labels == Syllable_name.lower()
Syllables = Data[ind]
return Syllables
def print_loss(hist):
plt.figure()
plt.plot(hist.history['loss'], 'r', hist.history['val_loss'], 'b')
plt.xlabel('Epochs')
plt.ylabel('loss')
plt.title('model loss')
plt.legend(['loss','val_loss'])
plt.figure()
plt.plot(hist.history['accuracy'], 'r', hist.history['val_accuracy'], 'b')
plt.xlabel('Epochs')
plt.ylabel('accuracy')
plt.title('model accuracy')
plt.legend(['accuracy','val_accuracy'])
def plot_multiple_imgs(X,y,nrow=2,ncol=2,figsize=(13,7),preds=None,skip=0,title = 'plot_title',prob = None):
fig,ax = plt.subplots(nrows=nrow,ncols=ncol,figsize=figsize)
fig.subplots_adjust(hspace=0.2, wspace=0.1)
for i in range(nrow*ncol):
if preds is not None and i >= len(preds):
continue
ax[i//ncol,i%ncol].pcolormesh(20*np.log10(X[skip+i]))
# ax[i//ncol,i%ncol].pcolormesh((X[skip+i]))
ax[i//ncol,i%ncol].set_xticks([])
ax[i//ncol,i%ncol].set_yticks([])
if prob is None:
ax[i//ncol,i%ncol].set_title([i])
else:
ax[i//ncol,i%ncol].set_title('im ' + str(i) +', prob = ' + str(round(prob[i][preds[i]],3)))
if preds is not None:
ax[i//ncol,i%ncol].text(0.85, 0.1, str(preds[skip+i]), transform=ax[i//ncol,i%ncol].transAxes,
color='yellow' if y[skip+i]==preds[skip+i] else 'red',weight='bold')
ax[i//ncol,i%ncol].text(0.05, 0.1, str(y[skip+i]), color='blue',transform=ax[i//ncol,i%ncol].transAxes,weight='bold')
else:
ax[i//ncol,i%ncol].text(0.05, 0.1, str(y[skip+i]), color='blue',transform=ax[i//ncol,i%ncol].transAxes,weight='bold')
# plt.show()
fig.suptitle(title,fontweight="bold", size=20)
Loading Data and build model¶
In [100]:
# %matplotlib qt5
img_rows, img_cols = 64, 64 #choosing resize values
Data_united = np.load('Data_united.npy',allow_pickle = True)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
Data_united = np.array([scaler.fit_transform(i) for i in Data_united])
Labels_united = np.load('Labels_united.npy',allow_pickle = True)
Complex_syl = Take_Syllables('Complex',Data_united,Labels_united)
Frequency_steps_syl = Take_Syllables('Frequency steps',Data_united,Labels_united)
Chevron_syl = Take_Syllables('Chevron',Data_united,Labels_united)
Composite_syl = Take_Syllables('Composite',Data_united,Labels_united)
Noise_syl = Take_Syllables('99.0',Data_united,Labels_united)
two_syllable_syl = Take_Syllables('two-syllable',Data_united,Labels_united)
upward_syl = Take_Syllables('upward',Data_united,Labels_united)
flat_syl = Take_Syllables('flat',Data_united,Labels_united)
short_syl = Take_Syllables('short',Data_united,Labels_united)
harmonics_syl = Take_Syllables('harmonics',Data_united,Labels_united)
downward_syl = Take_Syllables('downward',Data_united,Labels_united)
complex_Labels = np.zeros((len(Complex_syl)))
freq_Labels = np.ones((len(Frequency_steps_syl)))
composite_Labels = 2*np.ones((len(Composite_syl)))
Noise_Labels = 3*np.ones((len(Noise_syl)))
model = build_model(num_classes=2)
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
model.summary()
plot_model(model)
#trying united syllables
freq_two = np.concatenate((Frequency_steps_syl,two_syllable_syl))
complex_chevron_flat = np.concatenate((Chevron_syl,Complex_syl,flat_syl))
freq_two_Labels = np.zeros((len(freq_two)))
complex_chevron_flat_Labels = np.ones((len(complex_chevron_flat)))
#plotting some examples
plot_multiple_imgs(Data_united,Labels_united,8,8,figsize = (20,20),skip = 500,title = 'Plotting some examples')
In [101]:
q,w = np.unique(Labels_united,return_counts = True)
df = pd.DataFrame({'h':q,'j':w})
df = df.sort_values('j')
df
Out[101]:
Complex vs Freq¶
In [102]:
x = np.concatenate((Complex_syl,Frequency_steps_syl))
x = np.expand_dims(x,axis = -1)
y = np.concatenate((complex_Labels,freq_Labels))
x = x.astype('float32')
# x /= 255
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.17, random_state=42) # 0.17 x 0.85 ~= 0.15
print(np.unique(y_train,return_counts = True))
print(np.unique(y_val,return_counts = True))
print(np.unique(y_test,return_counts = True))
from sklearn.utils import class_weight
class_weights = class_weight.compute_class_weight('balanced',
np.unique(y_train),
y_train)
y_train = keras.utils.to_categorical(y_train)
y_val = keras.utils.to_categorical(y_val)
model = build_model(num_classes=2)
model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
hist = model.fit(X_train,y_train ,validation_data = (X_val,y_val),
shuffle=True, epochs=200,batch_size = 128,class_weight=class_weights,
callbacks = set_callbacks(patience=20,name = 'model_weights'))
In [103]:
print_loss(hist)
predictions = model.predict(X_test)
pred = np.argmax(predictions,axis=1)
print(np.unique(pred,return_counts = True))
In [104]:
cm_analysis(y_test, pred, 'confusion matrix', [0,1])
check = y_test != pred
c = [i for i,j in enumerate(check) if j]
c_test = y_test[c]
c_x_test = X_test[c].reshape(X_test[c].shape[:-1])
c_pred = pred[c]
c_prob = predictions[c]
plot_multiple_imgs(c_x_test,c_test,4,4,(12,12),c_pred,title = 'complex = 0 , freq = 1',prob = c_prob)
# plot_multiple_imgs(c_x_test,c_test,5,5,(12,12),c_pred,title = 'freq = 0 , complex = 1, noise = 2, composite = 3, harmonic = 4')
plot_multiple_imgs(X_test.reshape(X_test.shape[:-1]),y_test,4,4,(12,12),pred,title = 'complex = 0 , freq = 1',prob = predictions)
Complex vs Freq with data augmentation¶
In [105]:
x = np.concatenate((Complex_syl,Frequency_steps_syl))
x = np.expand_dims(x,axis = -1)
y = np.concatenate((complex_Labels,freq_Labels))
x = x.astype('float32')
# x /= 255
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.17, random_state=42) # 0.17 x 0.85 ~= 0.15
print(np.unique(y_train,return_counts = True))
print(np.unique(y_val,return_counts = True))
print(np.unique(y_test,return_counts = True))
from sklearn.utils import class_weight
class_weights = class_weight.compute_class_weight('balanced',
np.unique(y_train),
y_train)
y_train = keras.utils.to_categorical(y_train)
y_val = keras.utils.to_categorical(y_val)
model = build_model(num_classes=2)
model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
datagen = ImageDataGenerator(
width_shift_range=0.02,
height_shift_range=0.02,
fill_mode='nearest')
hist = model.fit(datagen.flow(X_train, y_train, batch_size=128),
steps_per_epoch=len(X_train) / 128, validation_data = (X_val,y_val),
shuffle=True, epochs=200,class_weight=class_weights,
callbacks = set_callbacks(patience=20,name = 'model_weights'))
In [106]:
print_loss(hist)
predictions = model.predict(X_test)
pred = np.argmax(predictions,axis=1)
print(np.unique(pred,return_counts = True))
In [107]:
cm_analysis(y_test, pred, 'confusion matrix', [0,1])
check = y_test != pred
c = [i for i,j in enumerate(check) if j]
c_test = y_test[c]
c_x_test = X_test[c].reshape(X_test[c].shape[:-1])
c_pred = pred[c]
c_prob = predictions[c]
plot_multiple_imgs(c_x_test,c_test,4,4,(12,12),c_pred,title = 'complex = 0 , freq = 1',prob = c_prob)
# plot_multiple_imgs(c_x_test,c_test,5,5,(12,12),c_pred,title = 'freq = 0 , complex = 1, noise = 2, composite = 3, harmonic = 4')
plot_multiple_imgs(X_test.reshape(X_test.shape[:-1]),y_test,4,4,(12,12),pred,title = 'complex = 0 , freq = 1',prob = predictions)
Complex_Chevron_Flat Vs Frequency_Two syll¶
In [108]:
complex_chevron_flat = np.concatenate((Chevron_syl,Complex_syl,flat_syl))
freq_two = np.concatenate((Frequency_steps_syl,two_syllable_syl))
complex_chevron_flat_Labels = np.zeros((len(complex_chevron_flat)))
freq_two_Labels = np.ones((len(freq_two)))
In [109]:
x = np.concatenate((freq_two,complex_chevron_flat))
x = np.expand_dims(x,axis = -1)
y = np.concatenate((freq_two_Labels,complex_chevron_flat_Labels))
x = x.astype('float32')
# x /= 255
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.17, random_state=42) # 0.17 x 0.85 ~= 0.15
print(np.unique(y_train,return_counts = True))
print(np.unique(y_val,return_counts = True))
print(np.unique(y_test,return_counts = True))
from sklearn.utils import class_weight
class_weights = class_weight.compute_class_weight('balanced',
np.unique(y_train),
y_train)
y_train = keras.utils.to_categorical(y_train)
y_val = keras.utils.to_categorical(y_val)
model = build_model(num_classes=2)
model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
hist = model.fit(X_train,y_train ,validation_data = (X_val,y_val),
shuffle=True, epochs=200,batch_size = 128,class_weight=class_weights,
callbacks = set_callbacks(patience=20,name = 'model_weights'))
In [110]:
print_loss(hist)
predictions = model.predict(X_test)
pred = np.argmax(predictions,axis=1)
print(np.unique(pred,return_counts = True))
In [111]:
cm_analysis(y_test, pred, 'confusion matrix', [0,1])
check = y_test != pred
c = [i for i,j in enumerate(check) if j]
c_test = y_test[c]
c_x_test = X_test[c].reshape(X_test[c].shape[:-1])
c_pred = pred[c]
c_prob = predictions[c]
plot_multiple_imgs(c_x_test,c_test,4,4,(12,12),c_pred,title = 'complex = 0 , freq = 1',prob = c_prob)
# plot_multiple_imgs(c_x_test,c_test,5,5,(12,12),c_pred,title = 'freq = 0 , complex = 1, noise = 2, composite = 3, harmonic = 4')
plot_multiple_imgs(X_test.reshape(X_test.shape[:-1]),y_test,4,4,(12,12),pred,title = 'complex = 0 , freq = 1',prob = predictions)
In [112]:
x = np.concatenate((freq_two,complex_chevron_flat))
x = np.expand_dims(x,axis = -1)
y = np.concatenate((freq_two_Labels,complex_chevron_flat_Labels))
x = x.astype('float32')
# x /= 255
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.17, random_state=42) # 0.17 x 0.85 ~= 0.15
print(np.unique(y_train,return_counts = True))
print(np.unique(y_val,return_counts = True))
print(np.unique(y_test,return_counts = True))
from sklearn.utils import class_weight
class_weights = class_weight.compute_class_weight('balanced',
np.unique(y_train),
y_train)
y_train = keras.utils.to_categorical(y_train)
y_val = keras.utils.to_categorical(y_val)
model = build_model(num_classes=2)
model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
datagen = ImageDataGenerator(
width_shift_range=0.02,
height_shift_range=0.02,
fill_mode='nearest')
hist = model.fit(datagen.flow(X_train, y_train, batch_size=128),
steps_per_epoch=len(X_train) / 128, validation_data = (X_val,y_val),
shuffle=True, epochs=200,class_weight=class_weights,
callbacks = set_callbacks(patience=20,name = 'model_weights'))
In [113]:
print_loss(hist)
predictions = model.predict(X_test)
pred = np.argmax(predictions,axis=1)
print(np.unique(pred,return_counts = True))
In [114]:
cm_analysis(y_test, pred, 'confusion matrix', [0,1])
check = y_test != pred
c = [i for i,j in enumerate(check) if j]
c_test = y_test[c]
c_x_test = X_test[c].reshape(X_test[c].shape[:-1])
c_pred = pred[c]
c_prob = predictions[c]
plot_multiple_imgs(c_x_test,c_test,4,4,(12,12),c_pred,title = 'complex = 0 , freq = 1',prob = c_prob)
# plot_multiple_imgs(c_x_test,c_test,5,5,(12,12),c_pred,title = 'freq = 0 , complex = 1, noise = 2, composite = 3, harmonic = 4')
plot_multiple_imgs(X_test.reshape(X_test.shape[:-1]),y_test,4,4,(12,12),pred,title = 'complex = 0 , freq = 1',prob = predictions)
Freq Vs Chevron¶
In [115]:
Chevron_Labels = np.zeros((len(Chevron_syl)))
freq_Labels = np.ones((len(Frequency_steps_syl)))
x = np.concatenate((Chevron_syl,Frequency_steps_syl))
x = np.expand_dims(x,axis = -1)
y = np.concatenate((Chevron_Labels,freq_Labels))
x = x.astype('float32')
# x /= 255
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.17, random_state=42) # 0.17 x 0.85 ~= 0.15
print(np.unique(y_train,return_counts = True))
print(np.unique(y_val,return_counts = True))
print(np.unique(y_test,return_counts = True))
from sklearn.utils import class_weight
class_weights = class_weight.compute_class_weight('balanced',
np.unique(y_train),
y_train)
y_train = keras.utils.to_categorical(y_train)
y_val = keras.utils.to_categorical(y_val)
model = build_model(num_classes=2)
model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
hist = model.fit(X_train,y_train ,validation_data = (X_val,y_val),
shuffle=True, epochs=200,batch_size = 128,class_weight=class_weights,
callbacks = set_callbacks(patience=20,name = 'model_weights'))
In [116]:
print_loss(hist)
predictions = model.predict(X_test)
pred = np.argmax(predictions,axis=1)
print(np.unique(pred,return_counts = True))
In [117]:
cm_analysis(y_test, pred, 'confusion matrix', [0,1])
check = y_test != pred
c = [i for i,j in enumerate(check) if j]
c_test = y_test[c]
c_x_test = X_test[c].reshape(X_test[c].shape[:-1])
c_pred = pred[c]
c_prob = predictions[c]
plot_multiple_imgs(c_x_test,c_test,4,4,(12,12),c_pred,title = 'Chevron = 0 , freq = 1',prob = c_prob)
# plot_multiple_imgs(c_x_test,c_test,5,5,(12,12),c_pred,title = 'freq = 0 , complex = 1, noise = 2, composite = 3, harmonic = 4')
plot_multiple_imgs(X_test.reshape(X_test.shape[:-1]),y_test,4,4,(12,12),pred,title = 'complex = 0 , freq = 1',prob = predictions)
Freq Vs Chevron with data augmentation¶
In [118]:
Chevron_Labels = np.zeros((len(Chevron_syl)))
freq_Labels = np.ones((len(Frequency_steps_syl)))
x = np.concatenate((Chevron_syl,Frequency_steps_syl))
x = np.expand_dims(x,axis = -1)
y = np.concatenate((Chevron_Labels,freq_Labels))
x = x.astype('float32')
# x /= 255
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.17, random_state=42) # 0.17 x 0.85 ~= 0.15
print(np.unique(y_train,return_counts = True))
print(np.unique(y_val,return_counts = True))
print(np.unique(y_test,return_counts = True))
from sklearn.utils import class_weight
class_weights = class_weight.compute_class_weight('balanced',
np.unique(y_train),
y_train)
y_train = keras.utils.to_categorical(y_train)
y_val = keras.utils.to_categorical(y_val)
model = build_model(num_classes=2)
model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
datagen = ImageDataGenerator(
width_shift_range=0.02,
height_shift_range=0.02,
fill_mode='nearest')
hist = model.fit(datagen.flow(X_train, y_train, batch_size=128),
steps_per_epoch=len(X_train) / 128, validation_data = (X_val,y_val),
shuffle=True, epochs=200,class_weight=class_weights,
callbacks = set_callbacks(patience=20,name = 'model_weights'))
In [119]:
print_loss(hist)
predictions = model.predict(X_test)
pred = np.argmax(predictions,axis=1)
print(np.unique(pred,return_counts = True))
In [120]:
cm_analysis(y_test, pred, 'confusion matrix', [0,1])
check = y_test != pred
c = [i for i,j in enumerate(check) if j]
c_test = y_test[c]
c_x_test = X_test[c].reshape(X_test[c].shape[:-1])
c_pred = pred[c]
c_prob = predictions[c]
plot_multiple_imgs(c_x_test,c_test,4,4,(12,12),c_pred,title = 'Chevron = 0 , freq = 1',prob = c_prob)
# plot_multiple_imgs(c_x_test,c_test,5,5,(12,12),c_pred,title = 'freq = 0 , complex = 1, noise = 2, composite = 3, harmonic = 4')
plot_multiple_imgs(X_test.reshape(X_test.shape[:-1]),y_test,4,4,(12,12),pred,title = 'complex = 0 , freq = 1',prob = predictions)
Freq Vs complex vs composite¶
In [121]:
Complex_syl = Take_Syllables('Complex',Data_united,Labels_united)
Frequency_steps_syl = Take_Syllables('Frequency steps',Data_united,Labels_united)
# Chevron_syl = Take_Syllables('Chevron',Data_united,Labels_united)
Composite_syl = Take_Syllables('Composite',Data_united,Labels_united)
# Noise_syl = Take_Syllables('99.0',Data_united,Labels_united)
# two_syllable_syl = Take_Syllables('two-syllable',Data_united,Labels_united)
# flat_syl = Take_Syllables('flat',Data_united,Labels_united)
# harmonics_syl = Take_Syllables('harmonics',Data_united,Labels_united)
complex_Labels = np.zeros((len(Complex_syl)))
freq_Labels = np.ones((len(Frequency_steps_syl)))
composite_Labels = 2*np.ones((len(Composite_syl)))
# Noise_Labels = 3*np.ones((len(Composite_syl)))
In [122]:
x = np.concatenate((Complex_syl,Frequency_steps_syl,Composite_syl))
x = np.expand_dims(x,axis = -1)
y = np.concatenate((complex_Labels,freq_Labels,composite_Labels))
x = x.astype('float32')
# x /= 255
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.17, random_state=42) # 0.17 x 0.85 ~= 0.15
print(np.unique(y_train,return_counts = True))
print(np.unique(y_val,return_counts = True))
print(np.unique(y_test,return_counts = True))
from sklearn.utils import class_weight
class_weights = class_weight.compute_class_weight('balanced',
np.unique(y_train),
y_train)
y_train = keras.utils.to_categorical(y_train)
y_val = keras.utils.to_categorical(y_val)
model = build_model(num_classes=3)
model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
hist = model.fit(X_train,y_train ,validation_data = (X_val,y_val),
shuffle=True, epochs=200,batch_size = 128,class_weight=class_weights,
callbacks = set_callbacks(patience=20,name = 'model_weights'))
In [123]:
print_loss(hist)
predictions = model.predict(X_test)
pred = np.argmax(predictions,axis=1)
print(np.unique(pred,return_counts = True))
In [124]:
cm_analysis(y_test, pred, 'confusion matrix', [0,1,2])
check = y_test != pred
c = [i for i,j in enumerate(check) if j]
c_test = y_test[c]
c_x_test = X_test[c].reshape(X_test[c].shape[:-1])
c_pred = pred[c]
c_prob = predictions[c]
plot_multiple_imgs(c_x_test,c_test,4,4,(12,12),c_pred,title = 'Chevron = 0 , freq = 1 , compostie = 2 - errors',prob = c_prob)
# plot_multiple_imgs(c_x_test,c_test,5,5,(12,12),c_pred,title = 'freq = 0 , complex = 1, noise = 2, composite = 3, harmonic = 4')
plot_multiple_imgs(X_test.reshape(X_test.shape[:-1]),y_test,4,4,(12,12),pred,title = 'complex = 0 , freq = 1 , compostie = 2 - general',prob = predictions)
Freq Vs complex vs composite with data augmentation¶
In [125]:
Complex_syl = Take_Syllables('Complex',Data_united,Labels_united)
Frequency_steps_syl = Take_Syllables('Frequency steps',Data_united,Labels_united)
# Chevron_syl = Take_Syllables('Chevron',Data_united,Labels_united)
Composite_syl = Take_Syllables('Composite',Data_united,Labels_united)
# Noise_syl = Take_Syllables('99.0',Data_united,Labels_united)
# two_syllable_syl = Take_Syllables('two-syllable',Data_united,Labels_united)
# flat_syl = Take_Syllables('flat',Data_united,Labels_united)
# harmonics_syl = Take_Syllables('harmonics',Data_united,Labels_united)
complex_Labels = np.zeros((len(Complex_syl)))
freq_Labels = np.ones((len(Frequency_steps_syl)))
composite_Labels = 2*np.ones((len(Composite_syl)))
# Noise_Labels = 3*np.ones((len(Composite_syl)))
In [126]:
x = np.concatenate((Complex_syl,Frequency_steps_syl,Composite_syl))
x = np.expand_dims(x,axis = -1)
y = np.concatenate((complex_Labels,freq_Labels,composite_Labels))
x = x.astype('float32')
# x /= 255
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.17, random_state=42) # 0.17 x 0.85 ~= 0.15
print(np.unique(y_train,return_counts = True))
print(np.unique(y_val,return_counts = True))
print(np.unique(y_test,return_counts = True))
from sklearn.utils import class_weight
class_weights = class_weight.compute_class_weight('balanced',
np.unique(y_train),
y_train)
y_train = keras.utils.to_categorical(y_train)
y_val = keras.utils.to_categorical(y_val)
model = build_model(num_classes=3)
model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
datagen = ImageDataGenerator(
width_shift_range=0.02,
height_shift_range=0.02,
fill_mode='nearest')
hist = model.fit(datagen.flow(X_train, y_train, batch_size=128),
steps_per_epoch=len(X_train) / 128, validation_data = (X_val,y_val),
shuffle=True, epochs=200,class_weight=class_weights,
callbacks = set_callbacks(patience=20,name = 'model_weights'))
In [127]:
print_loss(hist)
predictions = model.predict(X_test)
pred = np.argmax(predictions,axis=1)
print(np.unique(pred,return_counts = True))
In [128]:
cm_analysis(y_test, pred, 'confusion matrix', [0,1,2])
check = y_test != pred
c = [i for i,j in enumerate(check) if j]
c_test = y_test[c]
c_x_test = X_test[c].reshape(X_test[c].shape[:-1])
c_pred = pred[c]
c_prob = predictions[c]
plot_multiple_imgs(c_x_test,c_test,4,4,(12,12),c_pred,title = 'Chevron = 0 , freq = 1 , compostie = 2 - errors',prob = c_prob)
# plot_multiple_imgs(c_x_test,c_test,5,5,(12,12),c_pred,title = 'freq = 0 , complex = 1, noise = 2, composite = 3, harmonic = 4')
plot_multiple_imgs(X_test.reshape(X_test.shape[:-1]),y_test,4,4,(12,12),pred,title = 'complex = 0 , freq = 1 , compostie = 2 - general',prob = predictions)
Freq Vs complex vs composite vs noise¶
In [129]:
Complex_syl = Take_Syllables('Complex',Data_united,Labels_united)
Frequency_steps_syl = Take_Syllables('Frequency steps',Data_united,Labels_united)
# Chevron_syl = Take_Syllables('Chevron',Data_united,Labels_united)
Composite_syl = Take_Syllables('Composite',Data_united,Labels_united)
Noise_syl = Take_Syllables('99.0',Data_united,Labels_united)
# two_syllable_syl = Take_Syllables('two-syllable',Data_united,Labels_united)
# flat_syl = Take_Syllables('flat',Data_united,Labels_united)
# harmonics_syl = Take_Syllables('harmonics',Data_united,Labels_united)
complex_Labels = np.zeros((len(Complex_syl)))
freq_Labels = np.ones((len(Frequency_steps_syl)))
composite_Labels = 2*np.ones((len(Composite_syl)))
Noise_Labels = 3*np.ones((len(Noise_syl)))
In [130]:
x = np.concatenate((Complex_syl,Frequency_steps_syl,Composite_syl,Noise_syl))
x = np.expand_dims(x,axis = -1)
y = np.concatenate((complex_Labels,freq_Labels,composite_Labels,Noise_Labels))
x = x.astype('float32')
# x /= 255
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.17, random_state=42) # 0.17 x 0.85 ~= 0.15
print(np.unique(y_train,return_counts = True))
print(np.unique(y_val,return_counts = True))
print(np.unique(y_test,return_counts = True))
from sklearn.utils import class_weight
class_weights = class_weight.compute_class_weight('balanced',
np.unique(y_train),
y_train)
y_train = keras.utils.to_categorical(y_train)
y_val = keras.utils.to_categorical(y_val)
model = build_model(num_classes=4)
model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
hist = model.fit(X_train,y_train ,validation_data = (X_val,y_val),
shuffle=True, epochs=200,batch_size = 128,class_weight=class_weights,
callbacks = set_callbacks(patience=20,name = 'model_weights'))
In [131]:
print_loss(hist)
predictions = model.predict(X_test)
pred = np.argmax(predictions,axis=1)
print(np.unique(pred,return_counts = True))
In [132]:
cm_analysis(y_test, pred, 'confusion matrix', [0,1,2,3])
check = y_test != pred
c = [i for i,j in enumerate(check) if j]
c_test = y_test[c]
c_x_test = X_test[c].reshape(X_test[c].shape[:-1])
c_pred = pred[c]
c_prob = predictions[c]
plot_multiple_imgs(c_x_test,c_test,4,4,(12,12),c_pred,title = 'Chevron = 0 , freq = 1 , compostie = 2, noise = 3 - errors',prob = c_prob)
# plot_multiple_imgs(c_x_test,c_test,5,5,(12,12),c_pred,title = 'freq = 0 , complex = 1, noise = 2, composite = 3, harmonic = 4')
plot_multiple_imgs(X_test.reshape(X_test.shape[:-1]),y_test,4,4,(12,12),pred,title = 'complex = 0 , freq = 1 , compostie = 2, noise = 3 - general',prob = predictions)
Freq Vs complex vs composite vs noise with data augmentation¶
In [133]:
Complex_syl = Take_Syllables('Complex',Data_united,Labels_united)
Frequency_steps_syl = Take_Syllables('Frequency steps',Data_united,Labels_united)
# Chevron_syl = Take_Syllables('Chevron',Data_united,Labels_united)
Composite_syl = Take_Syllables('Composite',Data_united,Labels_united)
Noise_syl = Take_Syllables('99.0',Data_united,Labels_united)
# two_syllable_syl = Take_Syllables('two-syllable',Data_united,Labels_united)
# flat_syl = Take_Syllables('flat',Data_united,Labels_united)
# harmonics_syl = Take_Syllables('harmonics',Data_united,Labels_united)
complex_Labels = np.zeros((len(Complex_syl)))
freq_Labels = np.ones((len(Frequency_steps_syl)))
composite_Labels = 2*np.ones((len(Composite_syl)))
Noise_Labels = 3*np.ones((len(Noise_syl)))
In [134]:
x = np.concatenate((Complex_syl,Frequency_steps_syl,Composite_syl,Noise_syl))
x = np.expand_dims(x,axis = -1)
y = np.concatenate((complex_Labels,freq_Labels,composite_Labels,Noise_Labels))
x = x.astype('float32')
# x /= 255
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.17, random_state=42) # 0.17 x 0.85 ~= 0.15
print(np.unique(y_train,return_counts = True))
print(np.unique(y_val,return_counts = True))
print(np.unique(y_test,return_counts = True))
from sklearn.utils import class_weight
class_weights = class_weight.compute_class_weight('balanced',
np.unique(y_train),
y_train)
y_train = keras.utils.to_categorical(y_train)
y_val = keras.utils.to_categorical(y_val)
model = build_model(num_classes=4)
model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
datagen = ImageDataGenerator(
width_shift_range=0.02,
height_shift_range=0.02,
fill_mode='nearest')
hist = model.fit(datagen.flow(X_train, y_train, batch_size=128),
steps_per_epoch=len(X_train) / 128, validation_data = (X_val,y_val),
shuffle=True, epochs=200,class_weight=class_weights,
callbacks = set_callbacks(patience=20,name = 'model_weights'))
In [135]:
print_loss(hist)
predictions = model.predict(X_test)
pred = np.argmax(predictions,axis=1)
print(np.unique(pred,return_counts = True))
In [137]:
cm_analysis(y_test, pred, 'confusion matrix', [0,1,2,3])
check = y_test != pred
c = [i for i,j in enumerate(check) if j]
c_test = y_test[c]
c_x_test = X_test[c].reshape(X_test[c].shape[:-1])
c_pred = pred[c]
c_prob = predictions[c]
plot_multiple_imgs(c_x_test,c_test,4,4,(12,12),c_pred,title = 'Chevron = 0 , freq = 1 , compostie = 2 - errors',prob = c_prob)
# plot_multiple_imgs(c_x_test,c_test,5,5,(12,12),c_pred,title = 'freq = 0 , complex = 1, noise = 2, composite = 3, harmonic = 4')
plot_multiple_imgs(X_test.reshape(X_test.shape[:-1]),y_test,4,4,(12,12),pred,title = 'complex = 0 , freq = 1 , compostie = 2 - general',prob = predictions)
In [ ]: